In other words, 98% of the variability in ŷ (y-hat, our dependent variable predictions) is capture by our model. It gives us an idea of the overall goodness of the fit.Īn adjusted R Square of 0.98 means our regression model can explain around 98% of the variation of the dependent variable Y (GDP) around the average value of the observations (the mean of our sample). Specifically, we should look at Adjusted R Square in our case, as we have more than one X variable. R Square is the most important among those, so we can start by looking at it. The information we got out of Excel’s Data Analysis module starts with the Regression Statistics. Now that we have our Summary Output from Excel let’s explore our regression model further. I rarely end up using all of them, but it’s easier to delete the ones we don’t need than rerun the whole thing. I will also mark all the additional options at the bottom. A new worksheet usually works best, as the tool inserts quite a lot of data. You can then consider placing the data on the same sheet or a new one. A 95% confidence interval is appropriate in most financial analysis scenarios, so we will not change this. Remember that Excel requires that all X variables are in adjacent columns.Īs I have selected the column Titles, it is crucial to mark the checkbox for Labels. Please, note that this is the same as running a single linear regression, the only difference being that we choose multiple columns for X Range. And in the X Range, we will select all X variable columns. The Y Range will include our dependent variable, GDP. Now it’s time to set some ranges and settings. Note, we use the same menu for both simple (single) and multiple linear regression models. Run it and pick Regression from all the options.
Look to the Data tab, and on the right, you will see the Data Analysis tool within the Analyze section. But it’s much easier with the Data Analysis Tool Pack, which you can enable from the Developer Tab -> Excel Add-ins. There are ways to calculate all the relevant statistics in Excel using formulas. Looking at the development over the periods, we can assume that GDP increases together with Education Spend and Employee Compensation. X2 – Unemployment Rate as % of the Labor Force Įven before we run our regression model, we notice some dependencies in our data.As a massive fan of Agatha Christie’s Hercule Poirot, let’s direct our attention to Belgium.Īs you can see in the table below, we have nineteen observations of our target variable (GDP), as well as our three predictor variables: The EU dataset gives us information for all member states of the union. I have also kept the links to the source tables to explore further if you want.
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All the relevant source data is within the model file for your convenience, which you can download below. We will obtain public data from Eurostat, the statistics database for the European Commission for this exercise. Now that we have this out of the way and expectations are set, let’s open Excel and get started! Sourcing our data
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The article aims to show you how to run multiple Regression in Excel and interpret the output, not to teach about setting up our model assumptions and choosing the most appropriate variables. I am not a statistician, and I do not claim that the selected dependent and independent variables are the right analysis choices. This article will take a practical look at modeling a Multiple Regression model for the Gross Domestic Product (GDP) of a country.īefore I start, let me add a short disclaimer. You can read our Regression Analysis in Financial Modeling article to gain more insight into the statistical concepts employed in the method and where it finds application within finance.
In a previous article, we explored Linear Regression Analysis and its application in financial analysis and modeling.
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